Shih-Hau Fang

Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments

This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted k -nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments

Positioning in indoor wireless environments is growing rapidly in importance and gains commercial interests in context-awareness applications. The essential challenge in localization is the severe fluctuation of receive signal strength (RSS) for the mobile client even at a fixed location. This work explores the major noisy source resulted from the multipath in an indoor wireless environment and presents an advanced positioning architecture to reduce the disturbance. Our contribution is to propose a novel approach to extract the robust signal feature from measured RSS which is provided by IEEE 802.11 MAC software so that the multipath effect can be mitigated efficiently. The dynamic multipath behavior, which can be modeled by a convolution operation in the time domain, can be transformed into an additive random variable in the logarithmic spectrum domain. That is, the convolution process becomes a linear and separable operation in the logarithmic spectrum domain and then can be effectively removed. To our best knowledge, this work is the first to enhance the robustness to a multipath fading condition, which is common in the environments of an indoor wireless LAN (WLAN) location fingerprinting system. Our approach is conceptually simple and easy to be implemented for practical applications. Neither a new hardware nor an extra sensor network installation is required. Both analytical simulation and experiments in a real WLAN environment demonstrate the usefulness of our approach to significant performance improvements. The numerical results show that the mean and the standard deviation of estimated error are reduced by 42% and 29%, respectively, as compared to the traditional maximum likelihood based approach. Moreover, the experimental results also show that fewer training samples are required to build the positioning models. This result can be attributed to that the location related information is effectively extracted by our algorithm.

Location Fingerprinting In A Decorrelated Space

We present a novel approach to the problem of the indoor localization in wireless environments. The main contribution of this paper is fourfold: 1) we show that by projecting the measured signal into a decorrelated signal space, the positioning accuracy is improved, since the cross correlation between each AP is reduced, 2) we demonstrate that this novel approach achieves a more efficient information compaction and provides a better scheme to reduce online computation (the drawback of AP selection techniques is overcome, since we reduce the dimensionality by combing features, and each component in the decorrelated space is the linear combination of all APs; therefore, a more efficient mechanism is provided to utilize information of all APs while reducing the computational complexity), 3) experimental results show that the size of training samples can be greatly reduced in the decorrelated space; that is, fewer human efforts are required for developing the system, and 4) we carry out comparisons between RSS and three classical decorrelated spaces, including Discrete Cosine Transform (DCT), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) in this paper. Two AP selection criteria proposed in the literature, MaxMean and InfoGain are also compared. Testing on a realistic WLAN environment, we find that PCA achieves the best performance on the location fingerprinting task.

Principal Component Localization in Indoor WLAN Environments

This paper presents a novel approach to building a WLAN-based location fingerprinting system. Our algorithm intelligently transforms received signal strength (RSS) into principal components (PCs) such that the information of all access points (APs) is more efficiently utilized. Instead of selecting APs, the proposed technique replaces the elements with a subset of PCs to simultaneously improve the accuracy and reduce the online computation. Our experiments are conducted in a realistic WLAN environment. The results show that the mean error is reduced by 33.75 percent, and the complexity by 40 percent, as compared to the existing methods. Moreover, several benefits of our algorithm are demonstrated, such as requiring fewer training samples and enhancing the robustness to RSS anomalies.

A dynamic system approach for radio location fingerprinting in wireless local area networks

This study focuses on the localization using Received Signal Strength (RSS) in dense multipath indoor environments. A dynamic system approach is proposed in the fingerprinting module, where the location is estimated from the state instead from RSS directly. The state is reconstructed from a temporal sequence of RSS samples by incorporating a proper memory structure based on Takens embedded theory. Then, a more accurate state-location correlation is estimated because the impact of the temporal variation due to multipath is considered. An indoor experiment in Wireless Local Area Networks (WLAN) shows the effectiveness of our approach.

An Enhanced ZigBee Indoor Positioning System With an Ensemble Approach

This paper presents a framework for ZigBee indoor positioning with an ensemble approach. This approach exploits the complementary advantages of various algorithms, weights the estimation results, and combines them to improve accuracy. This is achieved by dynamically analyzing the diverse patterns of inputs and combining base positioning algorithms with spatial dependent weights. The experiments were conducted in a realistic ZigBee sensor network. Results demonstrated that the proposed approach apparently achieves more accurate location estimation than the compared methods including the gradient-based search, linear squares approximation, multidimensional scaling, fingerprinting method, and a multi-expert system.

A Dynamic Hybrid Projection Approach for Improved Wi-Fi Location Fingerprinting

Projection techniques have been used in Wi-Fi location fingerprinting systems to improve positioning accuracy. However, environmental dynamics present challenges to projection design. Furthermore, current projection-optimization techniques used in positioning, such as principal component analysis (PCA) and multiple discriminant analysis (MDA), have both advantages and limitations. This paper proposes a dynamic hybrid projection (DHP) technique for improved Wi-Fi localization, in which the projection is dynamically determined by simultaneously exploiting the complementary advantages of PCA and MDA while avoiding their unfavorable properties. The main contribution of this work is twofold: First, this study provides a novel formulation of a hybrid projection, which embeds the discriminative power into PCA and compensates for the two numerical problems of MDA in a unified framework. Second, DHP dynamically adjusts the hybrid mechanism with additional information, regarding the online-input region. That is, the proposed projection is input dependent, whereas traditional projections are fixed after training. This study applies the proposed algorithm to location fingerprinting in a realistic indoor Wi-Fi environment. On-site experimental results demonstrate that DHP outperforms static projection schemes, reducing the 50th and 67th percentile localization errors by 24.73%-30% and 18.18%-19.51%, respectively, compared with PCA and MDA.

Cooperative multi-radio localization in heterogeneous wireless networks

Recent advances in mobile devices and ubiquity of wireless infrastructures create the opportunity to utilize heterogeneous wireless networks (HWNs) for localization. To efficiently exploit the spatial correlation embedded in the measurements from HWNs, we proposed two algorithms via a cooperative approach, called Direct Multi-Radio Fusion and Cooperative Eigen- Radio Positioning. The former discovers the spatial correlation after the information of measurements is reorganized to minimize the redundancy. The latter takes a further step to incorporate the spatial discrimination to estimate the location. We have implemented our algorithms for different wireless technologies involving the cellular GSM, DVB, FM and WLAN in realistic outdoor/indoor environments. The results show that the proposed algorithm reduces 44.19-48.88% of the mean error, as compared to the conventional approaches.

Calibration-Free Approaches for Robust Wi-Fi Positioning against Device Diversity: A Performance Comparison

Received signal strength (RSS) in Wi-Fi networks is commonly employed in indoor positioning systems; however, device diversity is a fundamental problem in such systems. This problem becomes more important in recent years due to the tremendous growth of new Wi-Fi devices, which perform differently in respect to the RSS values and degrade localization performance significantly. Several studies have proposed methods to improve the robustness of positioning systems against device diversity. This paper is primarily concerned with the performance of calibration-free approaches, including signal strength difference (SSD), hyperbolic location fingerprinting (HLF), and DIFF. The performance comparison is based on two Wi-Fi positioning systems in a 3-D indoor building, including a zero-configuration and a fingerprinting-based system. The results show that these calibration-free techniques perform much better than the original RSS with heterogeneous devices. However, the improvement in robustness is gained at the expense of losing some discriminative information. When the testing and training data are both measured from the same device, the performance of HLF and SSD is clearly below that of RSS in both systems. Although DIFF performs the best, it has to suffer from dealing with a space of large dimensions.

Is FM a RF-Based Positioning Solution in a Metropolitan-Scale Environment? A Probabilistic Approach With Radio Measurements Analysis

Recent mobile devices have already contained a low-cost FM receiving function due to the continuing improvements in the device manufacturing. This paper shows that positioning based on FM signal is an alternative radio option while meeting the FCC requirement. We present a probabilistic location system using a wider-covered and longer-lived FM infrastructure. The performance is evaluated in two different metropolitan-scale environments including National Taiwan University (NTU) and Wen-Shan rural area. Both results show that the FM based location system not only satisfies the FCC requirement but also provides a comparable or even better performance to GSM based solution. Moreover, we completely analyze the realistic radio measurements of FM and GSM from four perspectives including temporal variation, spatial separation, measurement correlation and spectrum allocation. Most FM measurements are observed to provide a lower temporal variation but a weaker spatial separation than GSM. Fortunately, we discover that the lack of spatial separation can be compensated by adding additional sensed channels. This property is useful especially for a rural area where the available GSM base stations are limited and distant. Furthermore, we point out that the spatial separation of GSM signal decreases when the signal level is weaker than -90 dBm. At such a condition, FM reports a better accuracy than GSM even with the fewer channels.